First genetic linkage map for comparative mapping and QTL screening of brill (Scophthalmus rhombus)

Aquaculture xxx (2013) xxx–xxx

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Aquaculture

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First genetic linkage map for comparative mapping and QTL screening of brill(Scophthalmus rhombus)☆

Miguel Hermida a, Silvia T. Rodríguez-Ramilo b,d, Ismael Hachero-Cruzado c, Marcelino Herrera c,Andrés A. Sciara a, Carmen Bouza a, Jesús Fernández d, Paulino Martínez a,⁎a Departamento de Genética, Facultad de Veterinaria, Universidad de Santiago de Compostela (USC), Campus de Lugo, 27002 Lugo, Spainb Departamento de Bioquímica, Genética e Inmunología, Facultad de Biología, Universidad de Vigo, 36310 Vigo, Spainc Instituto de Investigación y Formación Agraria y Pesquera de Andalucía (IFAPA), Centro Agua del Pino, Ctra. Cartaya-Puna Umbría s/n, 21450 Cartaya, Huelva, Spaind Departamento de Mejora Genética Animal, Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria, Ctra. Coruña Km 7.5, 28040 Madrid, Spain

☆ This paper is presented at the International SymposiXI, Auburn, AL, USA, June 24–30, 2012.⁎ Corresponding author. Tel./fax: +34 982822428.

E-mail address: [email protected] (P. Martíne

0044-8486/$ – see front matter © 2013 Elsevier B.V. Allhttp://dx.doi.org/10.1016/j.aquaculture.2013.02.041

Please cite this article as:Hermida,M., et al., 1Aquaculture (2013), http://dx.doi.org/10.101

a b s t r a c t
a r t i c l e i n f o
Article history:Received 2 July 2012Received in revised form 20 February 2013Accepted 25 February 2013Available online xxxx

Keywords:BrillScophthalmus rhombusTurbotCross-species microsatelliteGenetic mapGrowth-related QTL

Genetic maps constitute valuable tools to detect genomic regions associated with complex traits and to goforward to understand their genetic basis. Flatfish include several species of great commercial value forwhich increasing genomic resources are available including genetic maps and EST databases. Application ofcomparative mapping strategies to flatfish is relevant to obtain genetic information associated with produc-tive traits. The brill (Scophthalmus rhombus) is a flatfish species closely related to turbot (S. maximus) whosemeat is highly appreciated in the Spanish market. The Junta de Andalucía local Government has begun a pro-gram to adapt this species to captivity for its future production. In this study, we developed the first geneticmap of brill using the current turbot genetic map as starting point. This strategy enabled us to select a num-ber of homogeneously distributed markers in the turbot map and to apply cross-species microsatellite ampli-fication to obtain informative markers. Nearly two hundred microsatellites from the framework turbot mapwere used for validation, and 100 markers were finally informative for mapping. The parents and offspring ofthe two families (54 and 88, respectively) used to construct the genetic map were genotyped with this panel.All markers, except eleven, were successfully grouped and ordered in 24 linkage groups. Linkage groups andorder of markers were highly consistent with the previous turbot genetic map. Linkage map information wasused to carry out a preliminary study on growth-related QTL for body weight, length and Fulton's conditionfactor in the two families, as the main phenotypic traits of interest in this species.

© 2013 Elsevier B.V. All rights reserved.

1. Introduction

Linkage genetic maps constitute valuable tools for genomics stud-ies in farm fish species, including identification of genomic regionsassociated with complex traits of interest and to go beyond by under-standing their genetic basis (Canario et al., 2008; Danzmann andGharbi, 2007). Furthermore, genetic maps provide the support tostudy genome organization, constitute the anchorage for analyzinggenome evolution through comparative mapping and provide veryuseful landmarks for whole genome assembly (Kai et al., 2011;Naruse et al., 2009; Sanetra et al., 2009; Wang et al., 2011). To date,the highest whole genome and chromosomal information on fish isavailable atmodel species: zebra fish (Danio rerio), fugu (Fugu rubripes),Tetraodon (Tetraodon nigroviridis),medaka (Oryzias latipes) and stickle-back (Gasterosteus aculeatus) (http://www.ensembl.org). This genome

um on Genetics in Aquaculture

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First genetic linkagemap for co6/j.aquaculture.2013.02.041

information has been applied for comparative mapping and gene min-ing strategies to identify candidate genes related to productive traits(Bouza et al., 2012; Loukovitis et al., 2011; Rodríguez-Ramilo et al.,2011, 2012).

Genetic maps have been developed in several important farmedfish species, such as Atlantic salmon (Gilbey et al., 2004), Europeanseabass (Chistiakov et al., 2005), tilapia (Lee et al., 2005), giltheadsea bream (Franch et al., 2006), olive flounder (Kang et al., 2008),rainbow trout (Rexroad et al., 2008), channel catfish (Kucuktas etal., 2009), common carp (Cheng et al., 2009), grass carp (Xia et al.,2010), Japanese flounder (Castaño-Sánchez et al., 2010), Asian seabass (Wang et al., 2011) and half-smooth tongue sole (Song etal., 2012). One of the most important applications of geneticmaps is the identification of quantitative trait loci (QTL) for com-plex traits. This approach may eventually lead to the identificationof particular genes underlying productive traits, which could beused for gene assisted selection (GAS) programs. Alternatively,trait associated markers could be used for marker assisted selec-tion (MAS) programs, due to their correlation with the phenotypictrait variation.

mparativemapping andQTL screening of brill (Scophthalmus rhombus),

http://www.ensembl.org

http://dx.doi.org/10.1016/j.aquaculture.2013.02.041

mailto:[email protected]


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2 M. Hermida et al. / Aquaculture xxx (2013) xxx–xxx

A main goal of genetic breeding programs in aquaculture is to in-crease growth rate, because it decreases rearing time thus increasingbenefits. Nevertheless, the number of studies on growth related QTLin farmed fish is not large, mostly being focused in salmonids(Houston et al., 2008; McClelland and Naish, 2010; Moghadam etal., 2007; O'Malley et al., 2010; Sauvage et al., 2012), but also in spe-cies like tilapia (Cnaani et al., 2004), Asian sea bass (Wang et al.,2011) or European sea bass (Loukovitis et al., 2011) among others.Recently, several studies of QTL identification have been carried outin turbot for relevant aquaculture traits such as sex determination(Martínez et al., 2009), growth-related traits (Ruan et al., 2010;Sánchez-Molano et al., 2011), and disease resistance-related traits(Rodríguez-Ramilo et al., 2011, 2012). This has opened a new scenarioto similar studies in related species and has increased the knowledgeon the genetic basis of these traits in flatfish.

Flatfish (order Pleuronectiformes) are a relatively large group ofteleost with ca. 570 species of worldwide distribution, comprisingspecies of great commercial value. Several phylogenetic studies havebeen conducted using morphological and molecular data in thisgroup which have established the genetic relationships between spe-cies and families (Azevedo et al., 2008; Chapleau, 1993; Hensley,1997; Pardo et al., 2005). Application of new genomic tools such ascomparative mapping using model fish genomes as a bridge is highlyrelevant to understand the genetic basis of production traits such asgrowth, resistance to pathologies or sex determination mechanisms.In addition, this approach could aid to understand the singular meta-morphosis and reproductive genetic pathways of flatfish today notwell known (Cerdà et al., 2010). Four genetic linkage maps havebeen reported in flatfish: Japanese flounder (Coimbra et al., 2003), At-lantic halibut (Reid et al., 2007), half-smooth tongue sole (Song et al.,2012), and turbot (Bouza et al., 2007, 2008, 2012), and another one isbeing finished in Senegalese sole (de la Herrán, pers. comm.). Com-parative analysis and integration of flatfish genetic maps will bringa powerful tool for evolutionary research into Pleuronectiformesand will enhance the search of genome regions or candidate genes re-lated to productive traits.

The brill (Scophthalmus rhombus) is a flatfish species closely related toturbot (S. maximus), distributed from 30° to 64° N along the coastal areaat depths of 5–50 m (Vinagre et al., 2011). S. rhombus and S. maximusshare similar habitat and life-history features, though they show a quitedifferent diet and a little niche overlapping (Blanquer et al., 1992;Vinagre et al., 2011). It is a promising species for aquaculture in theSouthern Atlantic–Mediterranean coast, since it is adapted to warm cli-mate, shows high growth rates and its meat is highly appreciated in theSpanish market (Hachero-Cruzado et al., 2009). From 2002, the flatfishfarming group at IFAPA Center “Agua del Pino” (Cartaya, Huelva, Spain),in collaborationwith other Institutions, is focusing on the study of brill re-productive biology in captivity among other aspects, as a necessary pre-requisite for the sustainable culture of this species (Hachero-Cruzado etal., 2007).

In this study, we developed the first genetic map in the brill(S. rhombus) using the existing turbot genetic map as starting point.This strategy enabled us to select a number of homogeneously dis-tributed markers in the turbot map to obtain informative markersthrough cross-species microsatellite amplification, and to carry out apreliminary QTL screening on growth-related traits in the brill.

2. Materials and methods

2.1. Mapping families and DNA extraction

Two full-sib brill families of 54 and 88 offspring (Fam-1 andFam-2, respectively) were obtained at IFAPA Agua del Pino experi-mental aquaculture station. Brill larvae were obtained from artificiallyfertilized eggs of a domesticated broodstock adapted to captivity. Lar-val culture method was the same as in Hachero-Cruzado et al. (2009).

Please cite this article as:Hermida,M., et al., 1First genetic linkagemap for coAquaculture (2013), http://dx.doi.org/10.1016/j.aquaculture.2013.02.041

Brill families were kept under natural conditions of photoperiod inan open circulation system at 38–39‰ salinity, 5–8 ppm oxygenand 19–21 °C temperature. After the larval stage, fishes were fedad libitum with dry pellets (R-2 Europa 22™® from Skretting) andprogrammed automatic feeders (T-Drum Feeder™, Arvotec), sixtimes a day.

Fin tissue of analyzed individuals was cut and stored in 95% etha-nol. Genomic DNA was extracted from the preserved fin samplesusing a standard phenol–chloroform protocol.

Body weight and length were recorded in both families to evaluatethree growth-related traits: body weight, length and Fulton's condi-tion factor (FK). This is a measure of fish fatness computed as100 × We/Le3, where We is the body weight of the fish (in grams)and Le is the length of the fish (in centimeters). The age of evaluationwas one year post-hatching.

2.2. Microsatellite selection and genotyping

We took advantage of the high-resolution turbot map (Bouza etal., 2012) and the close relationship between brill and turbot (Pardoet al., 2005) to select and cross-amplify a number of homogeneouslydistributedmarkers in the turbot map following a sequential strategy.First, the panel of 98 homogeneously distributed turbot markers usedfor QTL identification (Martínez et al., 2009) was tested for cross am-plification and polymorphism in a small set of brill individuals. Aver-age distances between these markers were 18.4 cM and 13.8 cMaccording to the total and framework turbot genetic map lengths, re-spectively (Bouza et al., 2007, 2008), being below the minimum dis-tance proposed for QTL detection (Dekkers and Hospital, 2002). Themarkers which showed positive amplification and polymorphism inbrill, were genotyped in the parents of both brill families (Fam-1and Fam-2) to check for informativeness and subsequent mapping.The distribution of informative markers was evaluated in the turbotmap (Bouza et al., 2007, 2008) to look for the presence of gaps andgenome coverage. Additional markers were then selected to fill gapstrying to have available marker every ~10 cM. We focused on themost polymorphic EST-linked markers available in turbot map dueto their higher evolutionary conservation (Navajas-Pérez et al.,2012). This procedure was repeated twice and led to a total of 100 in-formative markers from 198 tested. PCR amplifications were carriedout using the set of primers designed for turbot using reported tech-nical conditions with slight modifications (Bouza et al., 2002, 2008;Navajas-Pérez et al., 2012; Pardo et al., 2007). Genotype data wereobtained using an ABI 3730xl Genetic Analyzer and the GeneMapper4.0 software (Applied Biosystems).

2.3. Linkage analysis

Linkage map was constructed with JoinMap 3.0 (Van Ooijen andVoorrips, 2001) using the Cross Pollinator (CP) model with unknownlinkage phase. A chi-square test was carried out first in JoinMap toidentify deviations from the expected Mendelian segregation pat-terns. Bonferroni correction was also considered for multiple tests(Rice, 1989).

Markers were assigned into different linkage groups with a criticalLOD threshold >3.0. The order of markers in each linkage group wasestablished using LOD thresholds of 3.0 and maximum recombinationthresholds of 0.4. In only a few cases less restrictive parameters werechosen. The linkage group assignment was confirmed with thetwopoint option of Cri-Map (Green et al., 1990). Besides, marker or-ders within groups were also confirmed with the all option of thispackage. The Kosambi mapping function (Kosambi, 1944) was ap-plied to estimate the genetic distances in centimorgans.

An integrated linkage analysis was performed to construct a con-sensus map with data from the two families using JoinMap. The aver-age recombination frequencies (RF) and combined LOD scores were



3M. Hermida et al. / Aquaculture xxx (2013) xxx–xxx

applied to locate those loci in the consensus map which were mappedin more than one family. The consensus map was constructed usingthe same parameters of individual maps. Family and consensus link-age maps were graphed by Mapchart 2.2 (Voorrips, 2002).

The availability of common microsatellite markers in two familiesallowed for a comparative evaluation of meiotic recombination ratebetween families (within sex) and between sexes as reported in theturbot by Bouza et al. (2012). Common marker pairs for females ormales were scored in the two families to check for RF differences be-tween families. Also, commonmarker pairs were averaged within sexin the two families for intersex comparison. Only RF with LODscore > 3.0 were considered for comparisons. Normality of RF waschecked using Kolmogorov–Smirnov test. The significance of differ-ences was checked using t-tests.

2.4. QTL detection

Linkage map information was used to carry out a first approach toidentify growth-related QTL for body weight, length and Fulton's con-dition factor in the two families following the methodology reportedby Rodríguez-Ramilo et al. (2011, 2012). Summarizing, two differentmethodologies were implemented in the programs GridQTL (Seatonet al., 2006) and QTLMap (Gilbert et al., 2008). The former software(http://www.gridqtl.org.uk/) uses a linear regression (LR) methodol-ogy, considering the linkage phase between markers according topedigree information. The Haseman and Elston (1972) method forQTL linkage analysis was applied, and the genome and chromosome-wide significance thresholds at P = 0.05 and 0.01 were estimatedwith a permutation test of 10,000 iterations. The QTLmap software(http://dga7.jouy.inra.fr/qtlmap/) detects QTL through interval map-ping using a maximum likelihood (ML) test. To determine the genomeand chromosome-wide significance level, a simulation with 10,000iterations was performed for each trait and linkage group.

An outbreed full-sib model was used and QTL was considered sug-gestive when significance was between 5% and 1% at chromosome-wide level, and significant when significance was below 1% atchromosome-wide level or when significance was below 5% atgenome-wide level. These thresholds also allowed establishing a con-fidence interval around the estimated position of the QTL.

To detect associations between markers and traits a one wayANOVA was performed on the offspring phenotypes using marker ge-notypes. These analyses were carried out within the linkage groupswhere QTL were identified. A simple Bonferroni correction wasperformed to avoid false positives due to multiple marker testing.Each ANOVA also provided a corrected R2 value useful to estimatethe reduction of the overall phenotypic variance of the trait due tothe model fitting, thus R2 indicating the proportion of the trait vari-ance explained by a given marker.

3. Results

3.1. Microsatellite genotyping

Out of 98 markers originally included in the turbot panel for QTLscreening, 42 showed positive amplifications and were informativein at least one brill family and were used for mapping (Table S1).Then, another 100 microsatellites were evaluated trying to fill gapsusing turbot map as reference (Bouza et al., 2012), 58 of them beingvalidated for mapping (Table S1). Out of 198 initial microsatellitemarkers evaluated, 75 were EST-derived and 40 of them were finallyvalidated. Among the 98 discarded microsatellites, 54 showed poor orno amplification at all and 44 were homozygous for parents of bothfamilies. The number of markers initially selected was proportionalto the size of each turbot linkage group. Nevertheless, validatedmarkers were not homogeneously distributed among the differentgroups, and so the correspondence between number of markers and


linkage group size was partially lost. The proportion of validatedmarkers ranged from 100% at linkage group (LG) 22 (4 out of 4 eval-uated markers) to 17% at LG18 (only 1 out of 6 evaluated markers).The average distance between the selected set of markers in the tur-bot map (Bouza et al., 2012) was 13.0 cM.


Of the 100 selected markers (Table S1), 80 were informative inFam-1 and 90 in Fam-2 families (Table S2). Nine out of 80 markers(11.3%) exhibited significant segregation distortion from Mendelianexpectations (P b 0.05) in Fam-1, but none of them after Bonferronicorrection. In Fam-2, 23 out of 90 markers deviated from Mendeliansegregation (25.6%), three of them (3.3%) after correction for multipletests. These deviations are in the range previously reported in otherflatfish species (Bouza et al., 2012; Song et al., 2012). The proportionof microsatellite loci with null alleles was 3.7 and 5.5% in Fam-1 andFam-2, respectively, very similar to that previously described in tur-bot (Bouza et al., 2007, 2012) and in other species (Dakin and Avise,2004).

The genetic maps constructed from Fam-1 and Fam-2 familiescontained 65 and 77 markers (55 and 65 framework; LOD > 3.0) or-dered in 21 and 24 linkage groups, respectively (Table 1; Fig. 1). Therewere 15 and 13 markers that remained unlinked in Fam-1 and Fam-2respectively. The resulting consensus map derived from the two fam-ilies contained 70 framework markers ordered in 24 linkage groups.Each brill linkage group (SR) showed its correspondence with a spe-cific turbot group (SM) in nearly all cases. Only two linkage groupsin brill corresponded with a single turbot group (LG04_SM). The re-verse did not happen.

Also, at LG18_SM a single marker could be cross-amplified in brill,and thus, no correspondence could be established for this linkagegroup between both species. Seven brill groups showed a similar lengthto their correspondent turbot groups (LGs 1_SR, 2_SR, 3_SR, 7_SR, 9_SR,11_SR, 13_SR). The linkage groups of brill ranged from 1.3 (LG04_SRb)to 101.3 (LG01_SR) cM length. The number of markers per linkagegroup ranged from 2 (several groups) to 7 (LG09_SR). Inter-marker dis-tances were 11.5 ± 1.3 cM and 12.1 ± 1.1 cM for framework and totalmap, respectively. Framework and total map lengths were 558.0 cMand 739.4 cM, respectively. Genome length was estimated using inter-marker distances according to the method by Hubert and Hedgecock(2004). Framework and total genome length were 1140.3 cM and1384.7 cM, respectively, and consequently, genome coverage was48.9% for framework and 57.5% for total brill maps.

Order of markers within groups was highly consistent with theprevious turbot genetic map (Fig. 1). Only small discrepanciesscattered across different linkage groups were found between bothmaps. Most of them were due to interchanged positions betweentwo non-framework markers (LGs 2_SR, 4_SRa, 7_SR, 13_SR and17_SR). There was only one discrepancy involving two frameworkmarkers in LG 9_SR, but the distance between them was low(~7 cM). When the turbot original order was specified with thefixed-order option in JoinMap to compare the order consistency inthe brill regarding turbot map, the chi-square value of goodness-of-fit was 5 times worse, thus supporting the adjustment of brillorder to data. Moreover, only one marker, Sma-USC282, which waspositioned at LG16_SM was associated to a different linkage groupin brill (LG05_SR). The genetic markers linked in the brill mapspanned a very similar length in the turbot map (714.9 cM; Bouzaet al., 2012). All of the above data suggest a remarkable stabilityboth in marker position and in the recombination frequency betweengenetic maps in both species. This fact will facilitate increasing thedensity and genome coverage of brill map by cross-amplifyingmarkers from the most recent turbot map (Bouza et al., 2012).

The recombination frequency (RF) distribution fitted to normalityin both family maps and in the consensus map. RF was compared


http://www.gridqtl.org.uk/

http://dga7.jouy.inra.fr/qtlmap/


Table 1Number of markers and length for each linkage group in the family and consensus brill (Scophthalmus rhombus) maps.

LG Consensus Fam-1 Fam-2 Genome lengthconsensus map

FW LOD b 3 UL Total FW LOD b 3 UL Total FW LOD b 3 UL Total FW Total

01_SR 5 5 5 5 5 5 101.3 101.302_SR 5 1 6 4 4 5 1 6 29.4 70.903_SR 5 1 6 5 1 6 5 5 72.2 72.204_SRa 2 1 3 2 1 3 2 2 14.2 27.704_SRb 2 2 2 2 1.3 1.305_SR 4 3 7 2 1 3 2 3 5 39.1 46.306_SR 2 1 3 2 1 3 2 1 3 4.8 4.807_SR 2 3 5 4 1 5 2 3 5 7.2 44.608_SR 3 3 2 2 3 3 2 209_SR 4 3 7 3 3 6 7 7 34 65.710_SR 2 1 3 2 2 2 1 3 4 411_SR 4 4 3 1 4 4 4 45.5 45.512_SR 2 1 3 2 2 2 1 3 5.8 33.313_SR 3 2 5 3 2 5 4 4 36.2 69.414_SR 3 1 1 5 3 1 1 5 2 2 1 5 30.4 40.215_SR 2 1 3 2 2 2 1 3 15.1 15.116_SR 2 2 4 2 2 2 2 4 6.6 6.617_SR 4 2 6 3 3 6 3 1 4 30.9 39.518_SMa 1 1 1 1 1 1 – –

19_SR 2 2 4 2 2 2 1 3 15.4 15.420_SR 2 2 2 2 2 2 3.3 3.321_SR 2 1 3 1 1 2 1 3 9.4 9.422_SR 2 1 1 4 2 1 3 2 2 11.3 11.323_SR 4 4 4 4 3 1 4 35.5 35.524_SR 2 2 2 2 2 2 3.1 3.1Total 70 19 11 100 55 10 15 80 65 12 13 90 558 739.4Mean 3.04 2.89 2.95 23.3 30.8

LG: linkage group; FW: framework; UL: unlinked; Fam: family.a No brill linkage group was obtained corresponding to tubot LG18_SM since only one marker could be cross-amplified.


between families within sex (i.e. within females and within males)and between sexes. Pair-wise RF differences between-females ofboth families were not significant, however, a slight significant differ-ence was detected between-males (P = 0.014). Pair-wise RF differ-ences between females (F) and males (M) were highly significant(P = 0.662 × 10−4), the ratio F:M being 1.5:1 (Fig. 2).

3.3. QTL detection

Consensus and family linkage maps with all markers were used tocarry out a preliminary analysis to identify growth-related QTL (bodyweight, length and Fulton's condition factor) in brill families. Asoutlined above, all brill linkage groups had their turbot counterpartexcept LG18_SM, where only onemarker was informative. The averageweight, length and Fulton's condition factor for families 1 and2 were 173.04 ± 8.68 and 142.49 ± 6.62 g, 22.35 ± 0.42 and20.63 ± 0.36 cm and 1.46 and 1.50, respectively.

QTL at genome-wide level were only detected in Fam-2 at LG24_SR for body weight with both methods and for length with theLR method (estimated position: 0 cM and confidence interval:0–3 cM for both traits and methods). Additionally, several suggestiveand significant QTL at chromosome-wide level were also identified.No QTL were detected for body weight in Fam-1. In addition, no QTLwere identified with the LR method for FK, probably due to the statis-tical properties of cubic variables. Using the LR approach, three sug-gestive (LG 02_SR, LG 03_SR and LG 14_SR) and one significant QTLat chromosome-wide level (LG 24_SR) were detected for weightand, three suggestive (LG 03_SR, LG 10_SR and LG 11_SR) and onesignificant QTL at chromosome-wide level (LG 24_SR) were detectedfor length. Using the ML methodology, two suggestive (LG 03_SRand LG 14_SR) and one significant QTL at chromosome-wide level(LG 24_SR) were detected both for weight and length, and one sug-gestive (LG 03_SR) and one significant at chromosome-wide level(LG_04_SRa) for Fulton's condition factor.


The LR method identified some QTL at chromosome-wide levelthat were not detected using the ML approach and vice versa. QTL af-fecting all traits were detected on LG 03_SR. In addition, concordancebetween both methods (LR and ML) was observed in the detectedQTL for weight and length on LGs 03_SR, 14_SR and 24_SR. Forconcordant QTL, the same significance (suggestive or significant atchromosome-wide level) was observed for both methodologies. Ad-ditionally, QTL were detected in similar positions in both families(e.g. LG 24_SR for body length).

For each linkage group with a suggestive or significant QTL, an asso-ciation analysis was performed between the phenotypic trait and all themarkers in that linkage group. Table 2 shows the markers significantlyassociated with growth-related QTL detected at genome-wide level.Two markers showed significant association with body weight andlength explaining from 8 to 12% of the phenotypic variance. Atchromosome-wide level (data not shown), no markers were detectedsignificantly associated for body weight and length in Fam-1, likelydue to the reduced informativeness of the involvedmarkers when treat-ed separately. Markers showing significant association with growth-related traits explained from 7 to 23% of the phenotypic variancewhen considering all the detected QTL at chromosome-wide level. Themarkers explaining the highest proportion of phenotypic variance forweight, length and Fulton's condition factor were Sma-USC77 (14%),Sma-USC253 (14%) and Sma-USC30 (23%), respectively.

4. Discussion

4.1. Microsatellite validation

In this study, 100 turbot microsatellites were validated from the198 tested in brill. This cross-amplification success is very similar toother studies previously reported in both species (Bouza et al.,2002; Iyengar et al., 2000). Validation was slightly lower for anony-mous microsatellites than for EST-associated ones, which is in accor-dance with the higher evolutionary conservation of EST-derived



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Please cite this article as:Hermida,M., et al., 1First genetic linkagemap for comparativemapping andQTL screening of brill (Scophthalmus rhombus),Aquaculture (2013), http://dx.doi.org/10.1016/j.aquaculture.2013.02.041


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Fig. 2.Male vs. female recombination frequency comparison for pairs of markers segre-gating from two families in brill (Scophthalmus rhombus).


microsatellites (Serapion et al., 2004). This shows the potential ofthese markers for cross-species genome anchoring constituting usefultools for comparative mapping with model and non-model fishes(Gross et al., 2008; Vogiatzi et al., 2011). The possibility of using thesame markers across different related species would speed up theconstruction of linkage maps and its comparison, (Kucuktas et al.,2009; Ma et al., 2011; Sanetra et al., 2009; Walter et al., 2004). Inthis sense, the identification of common regions associated with parti-cular productive traits in different species would be very valuable to ex-change information between species for breeding programs, confirmingsimilar observations in livestock animals (Mackay, 2001). Moreover,searching for adaptive variation in wild populations or footprints of do-mestication in farm strains and its comparison between species will befacilitated by the existence of commonmarkers in linkage maps (Bouckand Vision, 2007; Ng et al., 2005; Vasemägi et al., 2005; Vilas et al.,2010).


This is the first genetic linkage map of brill, a teleost flatfish be-longing to a large family of great importance for fisheries and aqua-culture. Turbot and brill are two closely related species pertaining tothe same genus (Azevedo et al., 2008; Pardo et al., 2005), althoughsome authors place the turbot in a different genus (Psetta maxima;Nielsen, 1986). They show a very similar karyotype both in number(n = 22) and chromosome morphology (Pardo et al., 2001), and esti-mated genetic distances both with microsatellites (Bouza et al., 2002)and allozymes (Blanquer et al., 1992) suggest their inclusion in thesame genus. Moreover, both species show a similar distribution range(Blanquer et al., 1992) and hybrids can be obtained between them indomestic conditions (Purdom and Thacker, 1980).

Table 2Markers significantly associated with growth-related QTL in brill (Scophthalmusrhombus).

Trait Family LG Method andQTL position(cM)

Marker Markerposition(cM)

R2 (%)

We Fam-2 24_SR LR: 0/ML: 0 Sma-USC210 0.00 11.98F8-I11/8/17 3.13 9.06

Le Fam-2 24_SR LR: 0 Sma-USC210 0.00 11.03F8-I11/8/17 3.13 8.33

LG: linkage group;We: Weight; Le: Length; LR: linear regression method; ML: maximumlikelihood method; R2 (%): proportion of the explained phenotypic variance.


The number of linkage groups of the consensus brill map exceededits haploid chromosome number. This is an expected observation con-sidering the discrepancy between genetic and cytogenetic maps usuallyreported when constructing linkage maps in fish, especially when thedensity of markers is not high (Franch et al., 2006; Lee et al., 2005; Maet al., 2011; Portnoy et al., 2010). All brill linkage groups showed theircorrespondent counterpart in turbot, excluding LG18_SM. Frameworkand total brill maps showed 49 and 58% genome coverage, althougharound 1/3 brill linkage groups showed similar length to their turbotcounterparts. Estimated genome length in brill showed very similarfigures to those obtained in the turbot map (framework genome:1140.3 cM vs 1193.4 cM; total genome: 1384.7 cM vs 1402.7).

With the available information there is no evidence of Robertsoniantranslocations betweendifferent linkage groups, which agreeswith kar-yotype data, and only a single marker in turbot LG5 mapped in a differ-ent linkage group in brill. This could suggest the existence of a terminaltranslocation between both groups, but evidences are still very weakand additional information will be required. The existence of severalunlinkedmarkers (11%) highlights the necessity of increasingmap den-sity to fill the gaps and to confirm their expected position according toturbot map information.

Within each linkage group, both the consensus map and the indi-vidual maps revealed extensive conservation of marker order. Whenthe order was altered, in most cases a non-framework marker was in-volved (LGs 2_SR, 4_SRa, 13_SR, and 17_SR). Moreover, in LGs 2_SR,4_SRa and 17_SR, the differences were located at terminal positions,which could reveal erroneous mapping of markers. It is well knownthat, in general, markers with insufficient linkage information tendto be placed at the extremities of linkage groups (Hauge et al.,1993). Only in LG9_SR the position of two framework markers wasinterchanged, but the distance was low (~7 cM) and could be relatedto sampling variance. Nevertheless, the order obtained is the bestfitting to the present data so an inversion rearrangement should notbe discarded, as suggested in other related species (Reid et al.,2007; Sanetra et al., 2009).

Differences in recombination between sexes is a widespread phe-nomenon in many fish species, and lower recombination rates inmale than in females is a common observation in teleost fish(Sakamoto et al., 2000; Waldbieser et al., 2001; Wang et al., 2007).Our finding of a RF F:M ratio of 1.5 is in line with the pattern foundin other microsatellite-based fish maps (Chistiakov et al., 2005;Kucuktas et al., 2009), and especially, is very close to the previous fig-ures reported in turbot by Bouza et al. (1.6; 2007, 2012) and Ruan etal. (1.3; 2010). As observed in turbot (Bouza et al., 2012), insightsof RF differences between males but not between females weredetected. These differences constitute an important factor to be con-sidered when maps are applied for QTL identification and markerassisted selection programs.

4.3. QTL detection

A genome-wide analysis identified only one genomic region signifi-cantly related to body weight and length. However, under a less strictthreshold level several genomic regions controlling growth-relatedtraits (length, weight and FK) were identified. In spite of its low density,genetic markers were rather evenly distributed, thus providing an im-portant genomic coverage. The agreement between both approaches(LR and ML) and the detection of the same QTL in different familiesgive consistency to the observed results. Both statistical approacheswere highly concordant for weight and length QTL identification, butonly the ML approach was efficient to detect QTL related to FK. Thisobservation agrees with Kao's suggestion (2000) which consideredthe ML approach more reliable. Some linkage groups allocating QTLfor growth-related traits in brill were also identified in turbot bySánchez-Molano et al. (2011). For bodyweight and length the involvedlinkage groups were 3 and 14, and for Fulton's condition factor were




linkage groups 3 and 4. However, the concordance of QTL positions wasrather reduced between both studies. This is likely due to the reducednumber of markers in brill, which determined several markers to beunlinked increasing map gaps, and also, because map positions in thepresent study may be not definitive.

Markers showing significant association with growth-related traitsexplained up to 23% of the phenotypic variance. Remarkably, the mark-er Sma-USC30 at LG3_SR, explaining 23% of the phenotypic variance forFulton's condition factor, was also detected by Sánchez-Molano et al.(2011) and explained up to 11% of the phenotypic variance for thesame trait in turbot. In addition, the marker Sma-USC50 at LG16_SR,where suggestive QTL were identified when analyzing data fromFam-1 and Fam-2 together, explained up to 8% and 13% of the pheno-typic variance for body weight in brill and turbot, respectively. Thesedata provide new evidences supporting the trans-specific conservationof genomic associations for productive traits (Mackay, 2001).

Considering the high interest of these two common growth asso-ciated regions in brill and turbot, we tried a comparative mapping ap-proach using model fish genomes as a bridge (www.ensembl.org).Neither Sma-USC50 nor Sma-USC30 containing sequences showedsignificant hits with any of the five model fish genomes, likely dueto their anonymous condition. However, two conserved markers lo-cated close to Sma-USC50 in the turbot genetic map (Sma-USC223and Sma-E183) showed significant homology (E-value b 10−5) withstickleback LG4, medaka LG23 and Tetraodon LG19. Interestingly, acluster of key growth-related genes such as Insulin-like growth factorI (IGF-I), Myogenic factor 5 (MYF5) and Fibroblast growth factor 6(FGF6) are located in the proximity of these markers. As it is widelyknown, IGF-I is a mitogenic polypeptide that plays an essential rolein development and somatic growth of vertebrates (Moriyama et al.,2000); FGF6 is a member of the fibroblast growth factor family accu-mulating almost exclusively in the myogenic lineage which regulatesmuscle differentiation and proliferation of myogenic stem cells(Armand et al., 2005); and MYF5 is a member of the Myogenic Regu-latory Factors (MRFs), a group of conserved and partially redundantfactors, that plays key roles in muscular differentiation and growth(Wang et al., 1996). Although preliminary, our results suggest a con-servedmuscle growth regulatory genomic region in brill and turbot atLG16. Further analysis should be carried out in order to determine thelocation of these candidate genes and their specific association withgrowth-related traits.

5. Conclusions

The reported brill genetic map, although including a low numberof markers, provided an important coverage of brill genome becauseof the homogeneous distribution of the markers selected from turbotmap. A remarkable conservation of linkage groups and marker orderwas observed between both species, which will facilitate increasingmap coverage in the future through cross-amplification of turbotmarkers. This map was efficient to detect growth-related QTL inbrill in the first approach carried out in this species. Two growth-related QTL seemed to be conserved in homologous genomic regionsof turbot and brill, one of them apparently associated with a cluster ofrelevant growth-related genes. This map adds to the previouslyreported genetic maps in the order Pleuronectiform, and will facilitatecomparative mapping strategies to understand genome evolution ofthis order and to move genomic association data for productive traitsbetween flatfish species.

Supplementary data to this article can be found online at http://dx.doi.org/10.1016/j.aquaculture.2013.02.041.

Acknowledgments

This study was supported by the projects Consolider IngenioAquagenomics (CSD200700002), Spanish Ministerio de Ciencia e


Innovación (AGL2009-13273). We are indebted to Lucía Insua, andSonia Gómez for technical assistance. M. Herrera's post-doc contractis supported by the Operational Programme of the European SocialFund 2007–2013 of Andalusia.

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First genetic linkage map for comparative mapping and QTL screening of brill (Scophthalmus rhombus)